1. Field of the Invention
The present invention relates to apparatuses and methods for generating coefficient data used in an estimating equation that is used to convert a first information signal into a second information signal, or coefficient-seed data that is used as coefficient data in a generating equation for generating the coefficient data, apparatuses for converting the first information signal into the second information signal using the coefficient data or the coefficient-seed data, programs for allowing a computer to execute the method of generating coefficient data or coefficient-seed data, and to media having recorded the programs thereon.
More specifically, the present invention relates to techniques for obtaining coefficient data or coefficient-seed data that allows accurate reproduction of information data at a feature position having a predetermined feature using learning data including only information data at the feature position having the predetermined feature in a teacher signal when coefficient data for an estimating equation that is used to convert a first information signal into a second information signal or coefficient-seed data for generating the coefficient data is obtained by performing learning using a student signal corresponding to the first information signal and a teacher signal corresponding to the second information signal, so that the quality of an output composed of the second information signal is improved.
2. Description of the Related Art
Recently, various techniques for improving the resolution or sampling frequency of image signals or audio signals have been proposed. For example, it is known that when up conversion from a standard television signal having a standard or low resolution to an HDTV signal having a high resolution is performed or when sub-sampling interpolation is performed, more favorable results can be obtained by a method based on classification and adaptation than a method based on interpolation.
In the method based on classification and adaptation, when converting a standard television signal having a standard or low resolution (SD signal) into a signal having a high resolution (HD signal), a class to which pixel data at a subject pixel position in the HD signal is detected, and using coefficient data for an estimating equation associated with the class, pixel data at the subject pixel position in the HD signal is generated from a plurality of pieces of pixel data in the SD signal. The coefficient data for the estimating equation, used in the conversion involving classification, is determined for each class by performing learning, for example, by the least square method.
For example, according to Japanese Unexamined Patent Application Publication No. 2003-316760 (e.g., pp. 13 to 17 and FIGS. 4 to 7), using teacher data that serves as a teacher in learning and student data that serves as a student in learning, relationship between the teacher data and the student data is learned while weighting the teacher data and the student data based on activities (dynamic range) of prediction taps generated from the student data, obtaining tap coefficients (coefficient data) that improve the accuracy of prediction over the entire dynamic range of prediction taps.
As another example, according to Japanese Unexamined Patent Application Publication No. 2001-8056 (e.g., pp. 15 to 20 and FIGS. 9 to 12), prediction coefficients (coefficient data) for obtaining an image having a high S/N ratio from an image having a low S/N ratio is obtained by performing learning using high-S/N images that serve as teachers in learning and low-S/N images that serve as students in learning. First, prediction coefficients are obtained using learning data corresponding to all high-S/N pixels constituting high S/N images, and then prediction coefficients are obtained using only learning data corresponding to high-S/N pixels for which prediction error of predicted values obtained using the prediction coefficients obtained is not small, and similar processing is repeated. Accordingly, for all the high-S/N pixels constituting high-S/N images, prediction coefficients that reduce prediction error is obtained for each group of several high-S/N pixels.